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Mendeley readers
Attention Score in Context
| Title |
cclib: A library for package‐independent computational chemistry algorithms
|
|---|---|
| Published in |
Journal of Computational Chemistry, September 2007
|
| DOI | 10.1002/jcc.20823 |
| Pubmed ID | |
| Authors |
Noel M. O'boyle, Adam L. Tenderholt, Karol M. Langner |
| Abstract |
There are now a wide variety of packages for electronic structure calculations, each of which differs in the algorithms implemented and the output format. Many computational chemistry algorithms are only available to users of a particular package despite being generally applicable to the results of calculations by any package. Here we present cclib, a platform for the development of package-independent computational chemistry algorithms. Files from several versions of multiple electronic structure packages are automatically detected, parsed, and the extracted information converted to a standard internal representation. A number of population analysis algorithms have been implemented as a proof of principle. In addition, cclib is currently used as an input filter for two GUI applications that analyze output files: PyMOlyze and GaussSum. |
Mendeley readers
The data shown below were compiled from readership statistics for 763 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 7 | <1% |
| Poland | 2 | <1% |
| Italy | 2 | <1% |
| Spain | 2 | <1% |
| India | 2 | <1% |
| Argentina | 2 | <1% |
| Pakistan | 1 | <1% |
| Holy See (Vatican City State) | 1 | <1% |
| Chile | 1 | <1% |
| Other | 7 | <1% |
| Unknown | 736 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 161 | 21% |
| Researcher | 105 | 14% |
| Student > Master | 76 | 10% |
| Student > Doctoral Student | 53 | 7% |
| Professor > Associate Professor | 44 | 6% |
| Other | 145 | 19% |
| Unknown | 179 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 350 | 46% |
| Physics and Astronomy | 64 | 8% |
| Materials Science | 29 | 4% |
| Engineering | 21 | 3% |
| Biochemistry, Genetics and Molecular Biology | 14 | 2% |
| Other | 64 | 8% |
| Unknown | 221 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 06 May 2014.
All research outputs
#6,595,110
of 25,837,817 outputs
Outputs from Journal of Computational Chemistry
#452
of 2,251 outputs
Outputs of similar age
#24,259
of 84,316 outputs
Outputs of similar age from Journal of Computational Chemistry
#4
of 9 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 2,251 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done well, scoring higher than 79% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 84,316 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.